解广义特征值反问题的同伦方法

1.引言 我们讨论下列广义特征值反问题: (G)已知B是n×n阶对称半正定矩阵,λ=(λ_1,…,λ_(2n-1))~T∈R~(2n-1),且{λ_i}~(n_3),和{λ_i}_(n+1)~(2n-1)严格交错。问题是欲求一个实对称三对角n×n阶矩阵A,使得λ_1…,λ_n是Ax=λBx的特征值,λ_(n+1),…,λ_(2n-1)是A_(n-1)x=λB_(n-1)x的特征值,其中A_(n-1),B_(n-1)分别是矩阵A,B的前n-1阶主子阵。     

两个四元数自共轭矩阵乘积的特征值估计

设A和B都是四元数自共轭半正定矩阵，或者其中之一是正定的，而另一个是自共轭的，本文改进并推广了[2]对乘积AB的特征值估计.     

关于广义Minkowski不等式的一个注记

本文用反例证明了文[1]中与亚正定阵有关的一系列结论有错误。研究了文[2]中涉及亚正定阵的广义Minkowski不等式的证明过程，得到一个新结果，从而修正了这个不等式。     

泛正定阵与广义正定阵的关系

本文利用矩阵的稳定值,给出泛正定阵的等价条件,进一步得到泛正定阵与广义正定阵之间的一些关系．     

正定实方阵的Kronecker积

本文首先引进并讨论正定实方阵的广义特征值，进而导出正定实方阵的Ｋｒｏｎｅｃｋｅｒ积仍是正定的充要条件，最后作为应用给出正定实方阵的Ｈａｄａｍａｒｄ积仍是正定的一个充分条件．     

重特征值敏度的数值计算

一个结构系统的设计,往往归结为下述代数特征值问题:其中A(p)与B(p)为n×n实解析的对称矩阵,B(p)正定,λ(p)是特征值,x(p)是相应的特征向量. 设λ_1是问题(1.1)在点p=p~*的r重特征值,即存在矩阵X=(X_1,X_2)∈R~(n×n),     

关于两个厄米特矩阵乘积的特征值的估计问题

设A,B是两个任意的n阶厄米特矩阵(不假定A,B正定)。本文利用A,B的特征值给出了乘积矩阵AB的特征值的取值范围,基本上解决了对两个n阶厄米特矩阵乘积的特征值的估计,当A,B都是正定阵时,我们的结果大大地改进了[3]的结果。     

一类Hermitian鞍点矩阵的特征值估计

本文研究了一类大型稀疏Hermitian鞍点线性系统Az=(B E E* 0)(x y)=(f g)=b系数矩阵的特征值,其中B∈C~(p×p)是Hermitian正定阵矩阵,E∈C~(p×q)是列降秩.本文分别给出了该系数矩阵正特征值与负特征值界的一个估计式,同时通过数值算例验证本文所给出的特征值界的估计是合理且有效的.     

亚半正定阵左右逆特征值问题的进一步研究

１　引　言文［１］研究了亚半正定阵的左右逆特征值问题,它的更一般提法是问题Ｉ给定Ｘ、Ｚ使得其中Ｒｎ×ｍ表示全体ｎ×ｍ实阵的集合;即表示全体亚半正定阵集合［２］．文［１］得到了问题１有解的充要条件及解的通式,但从文［１］中主要定理给出的通式来看,子矩阵Ａ１３、Ａ１４及Ａ４３的表达式还没有得到,因此有必要对问题Ⅰ的通解作进一步的研究．本文将通过建立一个亚半正定阵的判定准则,圆满地解决以上问题． 为方便起见,本文用 及Ⅰ分别表示Ｒｎ×ｍ中秩为ｒ的矩阵集合、ｎ×正交矩阵集合及单位矩阵;而用　分别表示ｎ ×…     

矩阵方程X+A~*X~(-q)A=I(q>0)的Hermite正定解

1.引言 本文研究矩阵方程 X+A*X-qA=I (1)的Hermite正定解,其中I是一个n×n阶单位矩阵, A是一个n×n阶复矩阵, q是实数且q>0.q=1,q=2时的方程是从动态规划,随机过滤,控制理论和统计学中推导出来的,最近已有许多人对此进行了研究(见参考文献[1,2,4]),本文我们将研究方程(1)的解的存在性和解的性质,并讨论迭代求解及迭代解的收敛性. 对于Hermite矩阵X和Y,文中X≥Y表示X-Y是半正定的,X>y表示X-Y是正定的;对于方阵M,M*表示M的共轭转置,ρ(M)表示M的谱半径,λi(M)     

New fast divide‐and‐conquer algorithms for the symmetric tridiagonal eigenvalue problem

In this paper, two accelerated divide‐and‐conquer (ADC) algorithms are proposed for the symmetric tridiagonal eigenvalue problem, which cost O(N²r) flops in the worst case, where N is the dimension of the matrix and r is a modest number depending on the distribution of eigenvalues. Both of these algorithms use hierarchically semiseparable (HSS) matrices to approximate some intermediate eigenvector matrices, which are Cauchy‐like matrices and are off‐diagonally low‐rank. The difference of these two versions lies in using different HSS construction algorithms, one (denoted by ADC1) uses a structured low‐rank approximation method and the other (ADC2) uses a randomized HSS construction algorithm. For the ADC2 algorithm, a method is proposed to estimate the off‐diagonal rank. Numerous experiments have been carried out to show their stability and efficiency. These algorithms are implemented in parallel in a shared memory environment, and some parallel implementation details are included. Comparing the ADCs with highly optimized multithreaded libraries such as Intel MKL, we find that ADCs could be more than six times faster for some large matrices with few deflations. Copyright © 2016 John Wiley & Sons, Ltd.     

LU分解与广义Schmidt正交化方法

利用对称内积的Schmidt正交化方法证明了各阶主子式不为零对称阵的LDLT分解.引入两个向量组关于弱内积广义正交的概念,并构造了将两组含相同个数向量的线性无关组化为广义正交组的广义Schmidt正交化方法.最后应用这一方法证明了各阶主子式不为零矩阵的LDU分解及一些相关的结果.     

Generalized qd algorithm for block band matrices

The generalized qd algorithm for block band matrices is an extension of the block qd algorithm applied to a block tridiagonal matrix. This algorithm is applied to a positive definite symmetric block band matrix. The result concerning the behavior of the eigenvalues of the first and the last diagonal block of the matrix containing the entries q ^(k) which was obtained in the tridiagonal case is still valid for positive definite symmetric block band matrices. The eigenvalues of the first block constitute strictly increasing sequences and those of the last block constitute strictly decreasing sequences. The theorem of convergence, given in Draux and Sadik (Appl Numer Math 60:1300?C1308, 2010), also remains valid in this more general case.     

Divide and conquer algorithms for computing the eigendecomposition of symmetric diagonal-plus-semiseparable matrices

Three fast and stable divide and conquer algorithms to compute the eigendecomposition of symmetric diagonal-plus-semiseparable matrices are considered. AMS subject classification 15A18, 15A23, 65F15The research of the second and the third author was supported by the Research Council K.U. Leuven, to13.25cmproject OT/00/16 (SLAP: Structured Linear Algebra Package), by the Fund for Scientific Research –     

Separation of variables and the computation of Fourier transforms on finite groups, I

This paper introduces new techniques for the efficient computation of a Fourier transform on a finite group. We present a divide and conquer approach to the computation. The divide aspect uses factorizations of group elements to reduce the matrix sum of products for the Fourier transform to simpler sums of products. This is the separation of variables algorithm. The conquer aspect is the final computation of matrix products which we perform efficiently using a special form of the matrices. This form arises from the use of subgroup-adapted representations and their structure when evaluated at elements which lie in the centralizers of subgroups in a subgroup chain. We present a detailed analysis of the matrix multiplications arising in the calculation and obtain easy-to-use upper bounds for the complexity of our algorithm in terms of representation theoretic data for the group of interest.

Our algorithm encompasses many of the known examples of fast Fourier transforms. We recover the best known fast transforms for some abelian groups, the symmetric groups and their wreath products, and the classical Weyl groups. Beyond this, we obtain greatly improved upper bounds for the general linear and unitary groups over a finite field, and for the classical Chevalley groups over a finite field. We also apply these techniques to obtain analogous results for homogeneous spaces.

This is part I of a two part paper. Part II will present a refinement of these techniques which yields further savings.

     

Clarke Generalized Jacobian of the Projection onto Symmetric Cones

In this paper, we give an exact expression for Clarke generalized Jacobian of the projection onto symmetric cones, which generalizes and unifies the existing related results on second-order cones and the cones of symmetric positive semi-definite matrices over the reals. Our characterization of the Clarke generalized Jacobian exposes a connection to rank-one matrices.      

On inverse eigenvalue problems for block Toeplitz matrices with Toeplitz blocks

We propose an algorithm for solving the inverse eigenvalue problem for real symmetric block Toeplitz matrices with symmetric Toeplitz blocks. It is based upon an algorithm which has been used before by others to solve the inverse eigenvalue problem for general real symmetric matrices and also for Toeplitz matrices. First we expose the structure of the eigenvectors of the so-called generalized centrosymmetric matrices. Then we explore the properties of the eigenvectors to derive an efficient algorithm that is able to deliver a matrix with the required structure and spectrum. We have implemented our ideas in a Matlab code. Numerical results produced with this code are included.     

对称本原矩阵广义上指数的极矩阵

本文以伴随图的形早了对称本原矩阵和迹零对称本原矩阵的广义上指数的极矩阵。     

The generalized centro‐symmetric and least squares generalized centro‐symmetric solutions of the matrix equation AYB + CYTD = E

An n×n real matrix P is said to be a symmetric orthogonal matrix if P = P^?1 = P^T. An n × n real matrix Y is called a generalized centro‐symmetric with respect to P, if Y = PYP. It is obvious that every matrix is also a generalized centro‐symmetric matrix with respect to I. In this work by extending the conjugate gradient approach, two iterative methods are proposed for solving the linear matrix equation and the minimum Frobenius norm residual problem over the generalized centro‐symmetric Y, respectively. By the first (second) algorithm for any initial generalized centro‐symmetric matrix, a generalized centro‐symmetric solution (least squares generalized centro‐symmetric solution) can be obtained within a finite number of iterations in the absence of round‐off errors, and the least Frobenius norm generalized centro‐symmetric solution (the minimal Frobenius norm least squares generalized centro‐symmetric solution) can be derived by choosing a special kind of initial generalized centro‐symmetric matrices. We also obtain the optimal approximation generalized centro‐symmetric solution to a given generalized centro‐symmetric matrix Y₀ in the solution set of the matrix equation (minimum Frobenius norm residual problem). Finally, some numerical examples are presented to support the theoretical results of this paper. Copyright © 2011 John Wiley & Sons, Ltd.     

带有对称函数的实四数矩阵的广义数值半径

本文定义了带有对称函数的实四无数矩阵的广义数值半径并得到了它们所满足的不等式。